Source code for langchain_community.embeddings.awa

from typing import Any, Dict, List

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, root_validator

[docs]class AwaEmbeddings(BaseModel, Embeddings): """Embedding documents and queries with Awa DB. Attributes: client: The AwaEmbedding client. model: The name of the model used for embedding. Default is "all-mpnet-base-v2". """ client: Any #: :meta private: model: str = "all-mpnet-base-v2" @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that awadb library is installed.""" try: from awadb import AwaEmbedding except ImportError as exc: raise ImportError( "Could not import awadb library. " "Please install it with `pip install awadb`" ) from exc values["client"] = AwaEmbedding() return values
[docs] def set_model(self, model_name: str) -> None: """Set the model used for embedding. The default model used is all-mpnet-base-v2 Args: model_name: A string which represents the name of model. """ self.model = model_name self.client.model_name = model_name
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of documents using AwaEmbedding. Args: texts: The list of texts need to be embedded Returns: List of embeddings, one for each text. """ return self.client.EmbeddingBatch(texts)
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using AwaEmbedding. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.client.Embedding(text)